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Data-driven versus Hypothesis-driven approaches in cognitive neuroscience

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  • UserKarim Jerbi, PhD, Canada Research Chair (CRC) University of Montreal World_link
  • ClockFriday 28 May 2021, 16:15-18:00
  • HouseZoom meeting.

If you have a question about this talk, please contact Matthew Attwaters.

The spectacular success of artificial intelligence in recent years has triggered a surge in the use of data-driven methods in many fields of research. In cognitive neuroscience, brain decoding and encoding paradigms based on machine learning have recently attracted a lot of attention. Yet, amid strong claims about the endless power of AI and how it is going to revolutionize the way we do research, legitimate questions arise: Is the age of good-old hypothesis-driven neuroscience coming to an end? And what is the added value, if any, of machine-learning powered data-driven brain research? In this talk, I will begin with an introduction to the basics of machine learning and then provide illustrative examples of how my lab combines electrophysiological brain measurements, spectral analyses and machine learning to probe the link between oscillatory brain dynamics and various cognitive processes. I also promise to show a lot of pandas on my slides.

This talk is part of the Zangwill Club series.

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